Overview

Dataset statistics

Number of variables20
Number of observations193
Missing cells556
Missing cells (%)14.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.7 KiB
Average record size in memory189.6 B

Variable types

Numeric19
Categorical1

Alerts

alcohol consumption per capita is highly overall correlated with birth rate and 7 other fieldsHigh correlation
women marriage by 18 is highly overall correlated with men marriage by 18 and 8 other fieldsHigh correlation
men marriage by 18 is highly overall correlated with women marriage by 18High correlation
birth rate is highly overall correlated with alcohol consumption per capita and 10 other fieldsHigh correlation
life expectancy is highly overall correlated with women marriage by 18 and 9 other fieldsHigh correlation
death rate is highly overall correlated with alcohol consumption per capitaHigh correlation
agriculture occupation ratio is highly overall correlated with alcohol consumption per capita and 11 other fieldsHigh correlation
services occupation ratio is highly overall correlated with birth rate and 7 other fieldsHigh correlation
contraceptive prevalence is highly overall correlated with alcohol consumption per capita and 5 other fieldsHigh correlation
mothers mean age at first birth is highly overall correlated with alcohol consumption per capita and 10 other fieldsHigh correlation
health expenditures is highly overall correlated with agriculture occupation ratio and 3 other fieldsHigh correlation
gdp per capita is highly overall correlated with alcohol consumption per capita and 10 other fieldsHigh correlation
literacy total is highly overall correlated with alcohol consumption per capita and 9 other fieldsHigh correlation
physicians density is highly overall correlated with alcohol consumption per capita and 11 other fieldsHigh correlation
population below poverty is highly overall correlated with women marriage by 18 and 7 other fieldsHigh correlation
alcohol consumption per capita has 7 (3.6%) missing valuesMissing
tobacco use total has 31 (16.1%) missing valuesMissing
women marriage by 18 has 120 (62.2%) missing valuesMissing
men marriage by 18 has 141 (73.1%) missing valuesMissing
currently married women ages 15-49 has 3 (1.6%) missing valuesMissing
agriculture occupation ratio has 16 (8.3%) missing valuesMissing
industry occupation ratio has 19 (9.8%) missing valuesMissing
services occupation ratio has 31 (16.1%) missing valuesMissing
contraceptive prevalence has 53 (27.5%) missing valuesMissing
mothers mean age at first birth has 63 (32.6%) missing valuesMissing
health expenditures has 6 (3.1%) missing valuesMissing
literacy total has 37 (19.2%) missing valuesMissing
physicians density has 4 (2.1%) missing valuesMissing
population below poverty has 25 (13.0%) missing valuesMissing
population size has unique valuesUnique
alcohol consumption per capita has 5 (2.6%) zerosZeros

Reproduction

Analysis started2023-06-02 16:26:30.242985
Analysis finished2023-06-02 16:27:19.926274
Duration49.68 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

alcohol consumption per capita
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct165
Distinct (%)88.7%
Missing7
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean4.7025806
Minimum0
Maximum12.9
Zeros5
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:20.093249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0475
Q11.38
median3.96
Q37.71
95-th percentile10.9975
Maximum12.9
Range12.9
Interquartile range (IQR)6.33

Descriptive statistics

Standard deviation3.6868737
Coefficient of variation (CV)0.78401073
Kurtosis-1.0453984
Mean4.7025806
Median Absolute Deviation (MAD)3.025
Skewness0.42684341
Sum874.68
Variance13.593038
MonotonicityNot monotonic
2023-06-02T18:27:20.254345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
2.6%
7.45 3
 
1.6%
0.01 2
 
1.0%
4.09 2
 
1.0%
5.46 2
 
1.0%
0.02 2
 
1.0%
3.07 2
 
1.0%
5.74 2
 
1.0%
4.7 2
 
1.0%
0.25 2
 
1.0%
Other values (155) 162
83.9%
(Missing) 7
 
3.6%
ValueCountFrequency (%)
0 5
2.6%
0.01 2
 
1.0%
0.02 2
 
1.0%
0.04 1
 
0.5%
0.07 1
 
0.5%
0.08 1
 
0.5%
0.11 1
 
0.5%
0.13 1
 
0.5%
0.14 1
 
0.5%
0.16 1
 
0.5%
ValueCountFrequency (%)
12.9 1
0.5%
12.73 1
0.5%
11.93 1
0.5%
11.9 1
0.5%
11.88 1
0.5%
11.65 1
0.5%
11.44 1
0.5%
11.18 1
0.5%
11.05 1
0.5%
11 1
0.5%

tobacco use total
Real number (ℝ)

Distinct128
Distinct (%)79.0%
Missing31
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean20.332716
Minimum3.5
Maximum48.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:20.412971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile6.92
Q111.8
median20.55
Q326.7
95-th percentile37.57
Maximum48.5
Range45
Interquartile range (IQR)14.9

Descriptive statistics

Standard deviation9.8569509
Coefficient of variation (CV)0.4847828
Kurtosis-0.60644406
Mean20.332716
Median Absolute Deviation (MAD)7.8
Skewness0.37959665
Sum3293.9
Variance97.159482
MonotonicityNot monotonic
2023-06-02T18:27:20.568726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 4
 
2.1%
22 3
 
1.6%
8.5 3
 
1.6%
20.2 3
 
1.6%
14.3 3
 
1.6%
31.8 3
 
1.6%
10.6 2
 
1.0%
17.6 2
 
1.0%
16.2 2
 
1.0%
17.9 2
 
1.0%
Other values (118) 135
69.9%
(Missing) 31
 
16.1%
ValueCountFrequency (%)
3.5 1
0.5%
3.7 1
0.5%
5 1
0.5%
5.1 1
0.5%
5.5 1
0.5%
5.7 1
0.5%
6.8 1
0.5%
6.9 2
1.0%
7.3 1
0.5%
7.4 1
0.5%
ValueCountFrequency (%)
48.5 1
0.5%
44.1 1
0.5%
40.6 1
0.5%
39.8 1
0.5%
39.3 1
0.5%
39.2 1
0.5%
39 1
0.5%
38.2 1
0.5%
37.6 1
0.5%
37 1
0.5%

women marriage by 18
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)91.8%
Missing120
Missing (%)62.2%
Infinite0
Infinite (%)0.0%
Mean22.286301
Minimum0
Maximum61
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:20.722060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.16
Q112
median20.2
Q330.3
95-th percentile48.46
Maximum61
Range61
Interquartile range (IQR)18.3

Descriptive statistics

Standard deviation13.967257
Coefficient of variation (CV)0.62671936
Kurtosis0.3481529
Mean22.286301
Median Absolute Deviation (MAD)9.6
Skewness0.65430532
Sum1626.9
Variance195.08425
MonotonicityNot monotonic
2023-06-02T18:27:20.874350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.3 2
 
1.0%
14.9 2
 
1.0%
40.3 2
 
1.0%
30.5 2
 
1.0%
25.7 2
 
1.0%
0.1 2
 
1.0%
32.8 1
 
0.5%
43.4 1
 
0.5%
7.5 1
 
0.5%
29.1 1
 
0.5%
Other values (57) 57
29.5%
(Missing) 120
62.2%
ValueCountFrequency (%)
0 1
0.5%
0.1 2
1.0%
2.2 1
0.5%
3.8 1
0.5%
4.7 1
0.5%
5.3 1
0.5%
5.5 1
0.5%
5.8 1
0.5%
6 1
0.5%
6.1 1
0.5%
ValueCountFrequency (%)
61 1
0.5%
60.6 1
0.5%
53.7 1
0.5%
51.4 1
0.5%
46.5 1
0.5%
43.4 1
0.5%
40.3 2
1.0%
36 1
0.5%
35.5 1
0.5%
34 1
0.5%

men marriage by 18
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)78.8%
Missing141
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean4.7653846
Minimum0
Maximum22.2
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:21.057343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.31
Q11.9
median3.35
Q35.6
95-th percentile14.185
Maximum22.2
Range22.2
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation4.6726943
Coefficient of variation (CV)0.98054926
Kurtosis4.8105841
Mean4.7653846
Median Absolute Deviation (MAD)1.75
Skewness2.0829238
Sum247.8
Variance21.834072
MonotonicityNot monotonic
2023-06-02T18:27:21.249851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1.9 3
 
1.6%
2.8 2
 
1.0%
2.1 2
 
1.0%
1.6 2
 
1.0%
1.2 2
 
1.0%
3.2 2
 
1.0%
2.2 2
 
1.0%
5 2
 
1.0%
3.9 2
 
1.0%
5.6 2
 
1.0%
Other values (31) 31
 
16.1%
(Missing) 141
73.1%
ValueCountFrequency (%)
0 1
 
0.5%
0.1 1
 
0.5%
0.2 1
 
0.5%
0.4 1
 
0.5%
0.5 1
 
0.5%
0.7 1
 
0.5%
1.2 2
1.0%
1.4 1
 
0.5%
1.6 2
1.0%
1.9 3
1.6%
ValueCountFrequency (%)
22.2 1
0.5%
19.6 1
0.5%
17.1 1
0.5%
11.8 1
0.5%
10.8 1
0.5%
9.8 1
0.5%
9 1
0.5%
8.6 1
0.5%
8.4 1
0.5%
8.1 1
0.5%
Distinct144
Distinct (%)75.8%
Missing3
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean57.836842
Minimum31.5
Maximum80.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:21.388739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum31.5
5-th percentile45
Q152.3
median57.2
Q363.775
95-th percentile72
Maximum80.3
Range48.8
Interquartile range (IQR)11.475

Descriptive statistics

Standard deviation8.6460261
Coefficient of variation (CV)0.14948994
Kurtosis0.46546478
Mean57.836842
Median Absolute Deviation (MAD)5.45
Skewness-0.11356639
Sum10989
Variance74.753768
MonotonicityNot monotonic
2023-06-02T18:27:21.538485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.4 4
 
2.1%
59.6 3
 
1.6%
52.1 3
 
1.6%
55.9 3
 
1.6%
54.3 3
 
1.6%
57.1 3
 
1.6%
57.2 3
 
1.6%
54.8 2
 
1.0%
48.4 2
 
1.0%
60.8 2
 
1.0%
Other values (134) 162
83.9%
(Missing) 3
 
1.6%
ValueCountFrequency (%)
31.5 1
0.5%
32.7 1
0.5%
33.3 1
0.5%
37.1 1
0.5%
38.9 1
0.5%
40.3 1
0.5%
41.3 1
0.5%
43.2 1
0.5%
43.8 1
0.5%
45 2
1.0%
ValueCountFrequency (%)
80.3 1
0.5%
80.1 1
0.5%
77.9 1
0.5%
75.9 1
0.5%
74.6 1
0.5%
73.5 1
0.5%
72.6 2
1.0%
72.4 1
0.5%
72 2
1.0%
71.9 1
0.5%

birth rate
Real number (ℝ)

Distinct184
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.34228
Minimum6.61
Maximum46.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:21.691954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.61
5-th percentile8.234
Q110.86
median15.77
Q322.37
95-th percentile36.532
Maximum46.86
Range40.25
Interquartile range (IQR)11.51

Descriptive statistics

Standard deviation9.1568098
Coefficient of variation (CV)0.49921874
Kurtosis0.036096126
Mean18.34228
Median Absolute Deviation (MAD)5.44
Skewness0.9466459
Sum3540.06
Variance83.847166
MonotonicityNot monotonic
2023-06-02T18:27:21.850683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.3 2
 
1.0%
13.61 2
 
1.0%
10.8 2
 
1.0%
8.31 2
 
1.0%
26.01 2
 
1.0%
22.34 2
 
1.0%
10.33 2
 
1.0%
10.76 2
 
1.0%
8.87 2
 
1.0%
34.79 1
 
0.5%
Other values (174) 174
90.2%
ValueCountFrequency (%)
6.61 1
0.5%
6.87 1
0.5%
6.9 1
0.5%
6.95 1
0.5%
7 1
0.5%
7.12 1
0.5%
7.52 1
0.5%
7.97 1
0.5%
7.99 1
0.5%
8.12 1
0.5%
ValueCountFrequency (%)
46.86 1
0.5%
41.42 1
0.5%
40.72 1
0.5%
40.54 1
0.5%
40.27 1
0.5%
39.85 1
0.5%
39.64 1
0.5%
37.71 1
0.5%
37.07 1
0.5%
36.94 1
0.5%

life expectancy
Real number (ℝ)

Distinct176
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.027306
Minimum54.05
Maximum89.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:22.016056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum54.05
5-th percentile61.266
Q169.72
median75.4
Q378.61
95-th percentile83.324
Maximum89.64
Range35.59
Interquartile range (IQR)8.89

Descriptive statistics

Standard deviation6.8619367
Coefficient of variation (CV)0.09269467
Kurtosis-0.091588854
Mean74.027306
Median Absolute Deviation (MAD)4.47
Skewness-0.58320277
Sum14287.27
Variance47.086175
MonotonicityNot monotonic
2023-06-02T18:27:22.162612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.21 2
 
1.0%
79 2
 
1.0%
72.15 2
 
1.0%
78.61 2
 
1.0%
79.87 2
 
1.0%
81.71 2
 
1.0%
79.96 2
 
1.0%
70.48 2
 
1.0%
81.87 2
 
1.0%
72.72 2
 
1.0%
Other values (166) 173
89.6%
ValueCountFrequency (%)
54.05 1
0.5%
55.96 1
0.5%
56.12 1
0.5%
57.7 1
0.5%
59.07 1
0.5%
59.57 1
0.5%
59.71 1
0.5%
59.87 1
0.5%
60.22 1
0.5%
60.48 1
0.5%
ValueCountFrequency (%)
89.64 1
0.5%
86.51 1
0.5%
85 1
0.5%
84.05 1
0.5%
83.99 1
0.5%
83.83 1
0.5%
83.61 1
0.5%
83.54 1
0.5%
83.42 1
0.5%
83.39 1
0.5%

death rate
Real number (ℝ)

Distinct174
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5292228
Minimum1.42
Maximum15.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:22.320897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.42
5-th percentile3.886
Q15.71
median7.08
Q39.14
95-th percentile12.842
Maximum15.17
Range13.75
Interquartile range (IQR)3.43

Descriptive statistics

Standard deviation2.6758429
Coefficient of variation (CV)0.35539431
Kurtosis0.36722776
Mean7.5292228
Median Absolute Deviation (MAD)1.53
Skewness0.63493248
Sum1453.14
Variance7.1601353
MonotonicityNot monotonic
2023-06-02T18:27:22.465696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.77 3
 
1.6%
10.9 2
 
1.0%
7.82 2
 
1.0%
3.45 2
 
1.0%
4.2 2
 
1.0%
5.59 2
 
1.0%
9.66 2
 
1.0%
7.32 2
 
1.0%
8.17 2
 
1.0%
7.17 2
 
1.0%
Other values (164) 172
89.1%
ValueCountFrequency (%)
1.42 1
0.5%
1.62 1
0.5%
2.27 1
0.5%
2.83 1
0.5%
3.21 1
0.5%
3.45 2
1.0%
3.47 1
0.5%
3.85 1
0.5%
3.88 1
0.5%
3.89 1
0.5%
ValueCountFrequency (%)
15.17 1
0.5%
15.12 1
0.5%
14.92 1
0.5%
14.69 1
0.5%
14.31 1
0.5%
13.7 1
0.5%
13.27 1
0.5%
13.13 1
0.5%
12.98 1
0.5%
12.89 1
0.5%

agriculture occupation ratio
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct139
Distinct (%)78.5%
Missing16
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean29.193559
Minimum0
Maximum93.6
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:22.617230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.08
Q15.3
median18.1
Q347
95-th percentile80
Maximum93.6
Range93.6
Interquartile range (IQR)41.7

Descriptive statistics

Standard deviation27.313397
Coefficient of variation (CV)0.93559668
Kurtosis-0.78762094
Mean29.193559
Median Absolute Deviation (MAD)15.3
Skewness0.76053242
Sum5167.26
Variance746.02166
MonotonicityNot monotonic
2023-06-02T18:27:22.762197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 5
 
2.6%
17 4
 
2.1%
70 4
 
2.1%
11 3
 
1.6%
0.7 3
 
1.6%
65 3
 
1.6%
2 3
 
1.6%
85 2
 
1.0%
7 2
 
1.0%
71 2
 
1.0%
Other values (129) 146
75.6%
(Missing) 16
 
8.3%
ValueCountFrequency (%)
0 1
 
0.5%
0.2 1
 
0.5%
0.5 1
 
0.5%
0.7 3
1.6%
0.8 1
 
0.5%
0.9 1
 
0.5%
1 1
 
0.5%
1.1 2
1.0%
1.2 2
1.0%
1.3 2
1.0%
ValueCountFrequency (%)
93.6 1
 
0.5%
90 1
 
0.5%
86 1
 
0.5%
85 2
 
1.0%
82 1
 
0.5%
80 5
2.6%
79.2 1
 
0.5%
77.5 1
 
0.5%
76.9 1
 
0.5%
76 1
 
0.5%

industry occupation ratio
Real number (ℝ)

Distinct116
Distinct (%)66.7%
Missing19
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean19.35
Minimum1.9
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:22.917092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile5.13
Q113.05
median19.85
Q324.075
95-th percentile32.485
Maximum63
Range61.1
Interquartile range (IQR)11.025

Descriptive statistics

Standard deviation9.4134312
Coefficient of variation (CV)0.48648224
Kurtosis4.3591824
Mean19.35
Median Absolute Deviation (MAD)5.4
Skewness1.1989071
Sum3366.9
Variance88.612688
MonotonicityNot monotonic
2023-06-02T18:27:23.072018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 10
 
5.2%
14 4
 
2.1%
12 4
 
2.1%
10 4
 
2.1%
15 4
 
2.1%
11 3
 
1.6%
20.7 3
 
1.6%
7 3
 
1.6%
22 3
 
1.6%
18.3 3
 
1.6%
Other values (106) 133
68.9%
(Missing) 19
 
9.8%
ValueCountFrequency (%)
1.9 1
 
0.5%
2.3 1
 
0.5%
3.3 1
 
0.5%
3.9 1
 
0.5%
4.1 1
 
0.5%
4.4 1
 
0.5%
5 3
1.6%
5.2 1
 
0.5%
5.5 1
 
0.5%
6 1
 
0.5%
ValueCountFrequency (%)
63 1
0.5%
62.8 1
0.5%
49.6 1
0.5%
38 1
0.5%
36.9 1
0.5%
36 1
0.5%
35.1 1
0.5%
33.5 1
0.5%
33.2 1
0.5%
32.1 1
0.5%

services occupation ratio
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct142
Distinct (%)87.7%
Missing31
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean54.694198
Minimum4.1
Maximum95.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:23.224108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile19.045
Q139.925
median57.8
Q371.7
95-th percentile81.98
Maximum95.1
Range91
Interquartile range (IQR)31.775

Descriptive statistics

Standard deviation20.67457
Coefficient of variation (CV)0.37800299
Kurtosis-0.662909
Mean54.694198
Median Absolute Deviation (MAD)14.95
Skewness-0.45413787
Sum8860.46
Variance427.43785
MonotonicityNot monotonic
2023-06-02T18:27:23.374727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 3
 
1.6%
75 3
 
1.6%
19 2
 
1.0%
30 2
 
1.0%
73 2
 
1.0%
20 2
 
1.0%
47 2
 
1.0%
6 2
 
1.0%
81.6 2
 
1.0%
67.8 2
 
1.0%
Other values (132) 140
72.5%
(Missing) 31
 
16.1%
ValueCountFrequency (%)
4.1 1
0.5%
6 2
1.0%
13 1
0.5%
17 1
0.5%
17.5 1
0.5%
18 1
0.5%
19 2
1.0%
19.9 1
0.5%
20 2
1.0%
20.06 1
0.5%
ValueCountFrequency (%)
95.1 1
0.5%
93.9 1
0.5%
86.4 1
0.5%
86 1
0.5%
85.4 1
0.5%
84 1
0.5%
83.9 1
0.5%
83.5 1
0.5%
82 1
0.5%
81.6 2
1.0%

contraceptive prevalence
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct125
Distinct (%)89.3%
Missing53
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean49.162857
Minimum6.9
Maximum85.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:23.520184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile15.41
Q129.3
median52.4
Q366.825
95-th percentile79.615
Maximum85.5
Range78.6
Interquartile range (IQR)37.525

Descriptive statistics

Standard deviation21.227345
Coefficient of variation (CV)0.43177607
Kurtosis-1.1648786
Mean49.162857
Median Absolute Deviation (MAD)17.75
Skewness-0.25208672
Sum6882.8
Variance450.60019
MonotonicityNot monotonic
2023-06-02T18:27:23.675179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.3 3
 
1.6%
49.7 3
 
1.6%
18.9 2
 
1.0%
52.2 2
 
1.0%
73.9 2
 
1.0%
62.3 2
 
1.0%
16.6 2
 
1.0%
73 2
 
1.0%
33.5 2
 
1.0%
64.6 2
 
1.0%
Other values (115) 118
61.1%
(Missing) 53
27.5%
ValueCountFrequency (%)
6.9 1
0.5%
8.1 1
0.5%
10.9 1
0.5%
11 1
0.5%
11.5 1
0.5%
12.2 1
0.5%
13.7 1
0.5%
15.5 1
0.5%
16.6 2
1.0%
17.2 1
0.5%
ValueCountFrequency (%)
85.5 1
0.5%
84.5 1
0.5%
82.3 1
0.5%
81 1
0.5%
80.5 1
0.5%
80.4 1
0.5%
79.9 1
0.5%
79.6 1
0.5%
79 1
0.5%
77.9 1
0.5%

mothers mean age at first birth
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct85
Distinct (%)65.4%
Missing63
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean24.441538
Minimum18.1
Maximum32.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:23.832775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18.1
5-th percentile19.2
Q120.7
median23.25
Q328.35
95-th percentile31.055
Maximum32.8
Range14.7
Interquartile range (IQR)7.65

Descriptive statistics

Standard deviation4.1483495
Coefficient of variation (CV)0.16972539
Kurtosis-1.3294435
Mean24.441538
Median Absolute Deviation (MAD)3.5
Skewness0.27478516
Sum3177.4
Variance17.208804
MonotonicityNot monotonic
2023-06-02T18:27:23.977242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2 4
 
2.1%
19.4 4
 
2.1%
19.6 4
 
2.1%
22.4 3
 
1.6%
20.3 3
 
1.6%
21.9 3
 
1.6%
28.2 3
 
1.6%
20.8 3
 
1.6%
22.6 3
 
1.6%
29 3
 
1.6%
Other values (75) 97
50.3%
(Missing) 63
32.6%
ValueCountFrequency (%)
18.1 1
 
0.5%
18.5 1
 
0.5%
18.6 1
 
0.5%
19.1 2
1.0%
19.2 4
2.1%
19.3 1
 
0.5%
19.4 4
2.1%
19.5 1
 
0.5%
19.6 4
2.1%
19.8 1
 
0.5%
ValueCountFrequency (%)
32.8 1
0.5%
32.2 1
0.5%
31.9 1
0.5%
31.4 1
0.5%
31.3 1
0.5%
31.2 1
0.5%
31.1 1
0.5%
31 1
0.5%
30.9 1
0.5%
30.7 2
1.0%

health expenditures
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct91
Distinct (%)48.7%
Missing6
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean7.0887701
Minimum1.7
Maximum21.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:24.163866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile2.93
Q14.6
median6.5
Q39.05
95-th percentile12.14
Maximum21.5
Range19.8
Interquartile range (IQR)4.45

Descriptive statistics

Standard deviation3.2234577
Coefficient of variation (CV)0.45472736
Kurtosis2.3776854
Mean7.0887701
Median Absolute Deviation (MAD)2.2
Skewness1.13629
Sum1325.6
Variance10.39068
MonotonicityNot monotonic
2023-06-02T18:27:24.816011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.3 6
 
3.1%
7.6 6
 
3.1%
3.8 6
 
3.1%
6.3 5
 
2.6%
3.4 5
 
2.6%
4.1 4
 
2.1%
6.7 4
 
2.1%
6.5 4
 
2.1%
7.5 4
 
2.1%
4.4 4
 
2.1%
Other values (81) 139
72.0%
(Missing) 6
 
3.1%
ValueCountFrequency (%)
1.7 1
 
0.5%
2 1
 
0.5%
2.4 1
 
0.5%
2.5 1
 
0.5%
2.6 3
1.6%
2.7 1
 
0.5%
2.8 1
 
0.5%
2.9 1
 
0.5%
3 2
1.0%
3.3 2
1.0%
ValueCountFrequency (%)
21.5 1
0.5%
18.8 1
0.5%
18.4 1
0.5%
15.5 1
0.5%
13 1
0.5%
12.9 1
0.5%
12.8 1
0.5%
12.5 1
0.5%
12.2 2
1.0%
12 2
1.0%

gdp per capita
Real number (ℝ)

Distinct145
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21187.067
Minimum700
Maximum139100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:24.977325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum700
5-th percentile1500
Q14800
median13000
Q330800
95-th percentile61540
Maximum139100
Range138400
Interquartile range (IQR)26000

Descriptive statistics

Standard deviation23783.318
Coefficient of variation (CV)1.1225394
Kurtosis5.6929707
Mean21187.067
Median Absolute Deviation (MAD)9800
Skewness2.1314861
Sum4089104
Variance5.656462 × 108
MonotonicityNot monotonic
2023-06-02T18:27:25.122918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2100 4
 
2.1%
1500 3
 
1.6%
13800 3
 
1.6%
11900 3
 
1.6%
13700 3
 
1.6%
1600 3
 
1.6%
3200 3
 
1.6%
3700 3
 
1.6%
2200 3
 
1.6%
14600 3
 
1.6%
Other values (135) 162
83.9%
ValueCountFrequency (%)
700 1
 
0.5%
800 1
 
0.5%
1100 2
1.0%
1200 2
1.0%
1400 2
1.0%
1500 3
1.6%
1600 3
1.6%
1700 1
 
0.5%
1800 1
 
0.5%
1900 1
 
0.5%
ValueCountFrequency (%)
139100 1
0.5%
115700 2
1.0%
106000 1
0.5%
102500 1
0.5%
92900 1
0.5%
71000 1
0.5%
69700 1
0.5%
65700 1
0.5%
63700 1
0.5%
60100 1
0.5%

literacy total
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct105
Distinct (%)67.3%
Missing37
Missing (%)19.2%
Infinite0
Infinite (%)0.0%
Mean85.811538
Minimum26.8
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:25.286683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum26.8
5-th percentile45.95
Q178.575
median94.5
Q398.6
95-th percentile99.825
Maximum100
Range73.2
Interquartile range (IQR)20.025

Descriptive statistics

Standard deviation17.512454
Coefficient of variation (CV)0.20408042
Kurtosis1.4332783
Mean85.811538
Median Absolute Deviation (MAD)5.3
Skewness-1.4926953
Sum13386.6
Variance306.68606
MonotonicityNot monotonic
2023-06-02T18:27:25.432188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.8 6
 
3.1%
99 5
 
2.6%
99.9 4
 
2.1%
99.6 4
 
2.1%
100 4
 
2.1%
99.7 4
 
2.1%
98.4 4
 
2.1%
99.4 3
 
1.6%
89.1 3
 
1.6%
94.5 3
 
1.6%
Other values (95) 116
60.1%
(Missing) 37
 
19.2%
ValueCountFrequency (%)
26.8 1
0.5%
34.5 1
0.5%
35.5 1
0.5%
37.3 2
1.0%
37.4 1
0.5%
45.3 1
0.5%
45.8 1
0.5%
46 1
0.5%
47.7 1
0.5%
48.3 1
0.5%
ValueCountFrequency (%)
100 4
2.1%
99.9 4
2.1%
99.8 6
3.1%
99.7 4
2.1%
99.6 4
2.1%
99.5 1
 
0.5%
99.4 3
1.6%
99.2 2
 
1.0%
99.1 3
1.6%
99 5
2.6%

physicians density
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct152
Distinct (%)80.4%
Missing4
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean1.977619
Minimum0.02
Maximum8.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:25.599152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.07
Q10.42
median1.61
Q33.1
95-th percentile5.098
Maximum8.42
Range8.4
Interquartile range (IQR)2.68

Descriptive statistics

Standard deviation1.7598888
Coefficient of variation (CV)0.88990285
Kurtosis0.58826805
Mean1.977619
Median Absolute Deviation (MAD)1.26
Skewness0.95818856
Sum373.77
Variance3.0972087
MonotonicityNot monotonic
2023-06-02T18:27:25.742706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07 5
 
2.6%
0.2 4
 
2.1%
0.38 3
 
1.6%
0.19 3
 
1.6%
0.09 3
 
1.6%
0.05 3
 
1.6%
0.08 3
 
1.6%
0.17 2
 
1.0%
0.23 2
 
1.0%
3.47 2
 
1.0%
Other values (142) 159
82.4%
(Missing) 4
 
2.1%
ValueCountFrequency (%)
0.02 1
 
0.5%
0.04 1
 
0.5%
0.05 3
1.6%
0.06 1
 
0.5%
0.07 5
2.6%
0.08 3
1.6%
0.09 3
1.6%
0.1 1
 
0.5%
0.11 1
 
0.5%
0.12 1
 
0.5%
ValueCountFrequency (%)
8.42 1
0.5%
7.51 1
0.5%
7.09 1
0.5%
6.31 1
0.5%
6.11 1
0.5%
6.08 1
0.5%
6.06 1
0.5%
5.48 1
0.5%
5.29 1
0.5%
5.11 1
0.5%

population below poverty
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct146
Distinct (%)86.9%
Missing25
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean27.114286
Minimum0.2
Maximum82.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:25.889143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile5.175
Q114.8
median22.65
Q338.225
95-th percentile61.055
Maximum82.5
Range82.3
Interquartile range (IQR)23.425

Descriptive statistics

Standard deviation17.076402
Coefficient of variation (CV)0.62979354
Kurtosis0.37015212
Mean27.114286
Median Absolute Deviation (MAD)10
Skewness0.92338419
Sum4555.2
Variance291.60351
MonotonicityNot monotonic
2023-06-02T18:27:26.032809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.1 3
 
1.6%
20.1 2
 
1.0%
24.9 2
 
1.0%
21 2
 
1.0%
26.3 2
 
1.0%
9.4 2
 
1.0%
8.8 2
 
1.0%
12.7 2
 
1.0%
35 2
 
1.0%
48.6 2
 
1.0%
Other values (136) 147
76.2%
(Missing) 25
 
13.0%
ValueCountFrequency (%)
0.2 1
0.5%
0.6 1
0.5%
1.1 1
0.5%
4.1 1
0.5%
4.2 1
0.5%
4.3 1
0.5%
4.8 1
0.5%
4.9 1
0.5%
5 1
0.5%
5.5 1
0.5%
ValueCountFrequency (%)
82.5 1
0.5%
76.4 1
0.5%
70.7 1
0.5%
70 1
0.5%
67 1
0.5%
66.7 1
0.5%
64.6 1
0.5%
63 1
0.5%
62 1
0.5%
59.3 1
0.5%

population size
Real number (ℝ)

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40826502
Minimum9852
Maximum1.4131428 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-06-02T18:27:26.182616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9852
5-th percentile92757.4
Q12078820
median8940860
Q330518260
95-th percentile1.2618175 × 108
Maximum1.4131428 × 109
Range1.413133 × 109
Interquartile range (IQR)28439440

Descriptive statistics

Standard deviation1.4850203 × 108
Coefficient of variation (CV)3.6373928
Kurtosis73.937686
Mean40826502
Median Absolute Deviation (MAD)8455869
Skewness8.2803914
Sum7.879515 × 109
Variance2.2052852 × 1016
MonotonicityNot monotonic
2023-06-02T18:27:26.335384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39232003 1
 
0.5%
39993 1
 
0.5%
230842743 1
 
0.5%
26072217 1
 
0.5%
2133410 1
 
0.5%
5597924 1
 
0.5%
3833465 1
 
0.5%
247653551 1
 
0.5%
21779 1
 
0.5%
4404108 1
 
0.5%
Other values (183) 183
94.8%
ValueCountFrequency (%)
9852 1
0.5%
11639 1
0.5%
21779 1
0.5%
31597 1
0.5%
34892 1
0.5%
39993 1
0.5%
54817 1
0.5%
74656 1
0.5%
80966 1
0.5%
85468 1
0.5%
ValueCountFrequency (%)
1413142846 1
0.5%
1399179585 1
0.5%
339665118 1
0.5%
279476346 1
0.5%
247653551 1
0.5%
230842743 1
0.5%
218689757 1
0.5%
167184465 1
0.5%
141698923 1
0.5%
129875529 1
0.5%

continent
Categorical

Distinct6
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Africa
53 
Asia
47 
Europe
44 
North America
23 
Oceania
14 

Length

Max length13
Median length6
Mean length6.8549223
Min length4

Characters and Unicode

Total characters1323
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia
2nd rowEurope
3rd rowAfrica
4th rowEurope
5th rowAfrica

Common Values

ValueCountFrequency (%)
Africa 53
27.5%
Asia 47
24.4%
Europe 44
22.8%
North America 23
11.9%
Oceania 14
 
7.3%
South America 12
 
6.2%

Length

2023-06-02T18:27:26.486783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-02T18:27:26.638973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
africa 53
23.2%
asia 47
20.6%
europe 44
19.3%
america 35
15.4%
north 23
10.1%
oceania 14
 
6.1%
south 12
 
5.3%

Most occurring characters

ValueCountFrequency (%)
a 163
12.3%
r 155
11.7%
i 149
11.3%
A 135
10.2%
c 102
 
7.7%
e 93
 
7.0%
o 79
 
6.0%
u 56
 
4.2%
f 53
 
4.0%
s 47
 
3.6%
Other values (10) 291
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1060
80.1%
Uppercase Letter 228
 
17.2%
Space Separator 35
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 163
15.4%
r 155
14.6%
i 149
14.1%
c 102
9.6%
e 93
8.8%
o 79
7.5%
u 56
 
5.3%
f 53
 
5.0%
s 47
 
4.4%
p 44
 
4.2%
Other values (4) 119
11.2%
Uppercase Letter
ValueCountFrequency (%)
A 135
59.2%
E 44
 
19.3%
N 23
 
10.1%
O 14
 
6.1%
S 12
 
5.3%
Space Separator
ValueCountFrequency (%)
35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1288
97.4%
Common 35
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 163
12.7%
r 155
12.0%
i 149
11.6%
A 135
10.5%
c 102
 
7.9%
e 93
 
7.2%
o 79
 
6.1%
u 56
 
4.3%
f 53
 
4.1%
s 47
 
3.6%
Other values (9) 256
19.9%
Common
ValueCountFrequency (%)
35
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 163
12.3%
r 155
11.7%
i 149
11.3%
A 135
10.2%
c 102
 
7.7%
e 93
 
7.0%
o 79
 
6.0%
u 56
 
4.2%
f 53
 
4.0%
s 47
 
3.6%
Other values (10) 291
22.0%

Interactions

2023-06-02T18:27:16.480673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:31.460998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:33.698765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:36.118165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:38.424392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:41.257122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:44.014562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:46.956601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:49.359671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:51.664357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:54.224389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:56.633219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:58.858701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:01.304976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:04.113324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:06.474876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:08.911475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:11.312665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:14.167314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:16.604478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:31.573620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:33.815118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:36.231527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:38.545304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:41.420449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:44.166919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:47.074902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:49.479023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:51.780440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:54.346796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:56.741552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:58.984759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:01.751526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:04.234156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:06.658291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:09.034729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:11.429209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:14.281301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:16.717515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:31.683185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:33.916086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:36.411769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:38.725388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:41.575078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:44.335483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:47.189039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:49.591343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:51.905225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:54.478228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:56.867024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:59.114961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:01.882373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:04.347859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:06.776157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:09.168046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:11.537309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:14.415094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:16.886333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:31.797670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:34.034039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:36.519115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:39.018606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:41.718485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:44.457831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:47.306113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:49.708970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:52.060880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:54.607307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:56.982919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:59.290738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:02.007210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:04.462221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:06.886513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:09.288585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:11.645286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:14.539361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:16.992675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:31.898178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:34.149132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:36.622931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:39.125960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:41.844307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:44.573194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:47.414183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:49.814100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:52.161065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:54.710470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:57.095437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:59.398228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:02.123455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:04.573406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:07.001318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:09.468782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:11.749635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:14.646659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:17.109495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:32.018159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:34.256694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:36.742382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:39.252760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:41.981835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:44.715530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:47.524830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:49.931444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:52.276795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:54.831459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:57.206988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:59.510163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:02.240050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:04.691803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:07.125037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:09.582904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:11.862458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:14.760920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:17.243947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:32.140416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:34.496712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:36.858965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:39.407051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:42.144420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:44.874968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:47.713090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:50.069591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:52.401184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:54.954021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:57.332035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:59.638696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:02.432958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:04.824572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:07.263438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:09.716341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:11.984418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:14.884875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:17.371891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:32.263762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:34.615254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:36.974745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:39.563541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:42.291726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:45.033901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:47.831902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:50.193162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:52.773991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:55.083946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:57.449874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:59.762112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:02.556041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:04.951658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:07.386642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:09.825989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:12.519444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:15.012876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:17.498819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:32.385441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:34.732872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:37.087952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:39.654049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:42.425761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:45.191489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:47.954108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:50.311653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:52.890198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:55.247838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:57.572120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:59.891270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:02.669363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:05.078776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:07.511760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:09.946188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:12.716802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:15.121040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:17.612313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:32.493997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:34.847684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:37.190212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:39.750324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:42.564088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:45.330547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:48.076527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:50.423423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:53.000704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:55.355476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:57.674998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:00.000452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:02.789179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:05.238573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:07.632877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:10.060514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:12.834773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:15.228014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:17.738084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:32.609271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:35.026397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:37.302002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:39.867883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:42.729232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:45.734449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:48.202977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:50.595088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:53.125322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:55.471492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:57.790430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:00.132195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:02.906367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:05.369401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:07.760174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:10.182124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:12.966107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:15.346872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:17.852074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:32.728525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:35.138619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:37.423470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:40.034597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:42.835621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:45.875957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:48.314864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:50.699792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:53.229854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:55.589456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:57.933161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:00.242912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:03.023982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:05.475053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:07.874362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:10.294465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:13.093868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:15.509238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:17.979640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:32.857863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:35.267696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:37.546624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:40.209557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:42.962544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:46.061764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:48.439470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:50.819900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:53.349507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:55.707318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:58.048288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:00.366224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:03.159901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:05.588706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:08.051834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:10.421516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:13.233494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:15.628635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:18.107355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:32.990761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:35.400530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:37.716065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:40.373803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:43.126338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:46.201658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:48.565839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:50.937553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:53.471971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:55.835700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:58.171180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:00.502221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:03.282186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:05.715123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:08.179839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:10.548674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:13.389786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:15.758517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:18.236654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:33.110305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:35.518839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:37.832688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:40.497625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:43.268489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:46.330949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:48.694946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:51.067165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:53.587892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:55.961480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:58.283842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:00.696133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:03.415618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:05.851394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:08.311306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:10.681958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:13.534458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:15.893181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:18.409340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:33.231842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:35.636893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:37.946415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:40.638692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:43.431888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:46.462131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:48.825081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:51.189537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:53.782188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:56.094427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:58.409739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:00.806557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:03.559299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:05.966655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:08.432168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:10.822740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:13.646483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:16.013279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:18.534800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:33.355234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:35.763863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:38.073138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:40.794396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:43.579083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:46.596393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:48.978562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:51.314734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:53.895170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:56.217750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:58.525749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:00.942968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:03.694944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:06.095346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:08.558625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:10.966849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:13.765185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:16.130328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:18.649315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:33.463384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:35.867297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:38.178567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:40.922366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:43.712014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:46.713441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:49.116223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:51.427221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:53.991277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:56.331199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:58.631886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:01.050035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:03.863304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:06.211875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:08.671191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:11.075119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:13.870516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:16.250111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:18.770672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:33.574094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:35.996336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:38.299182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:41.120472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:43.846235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:46.825176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:49.229759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:51.535837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:54.098253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:56.441769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:26:58.734290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:01.176170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:03.985075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:06.344917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:08.780213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:11.182026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:13.983160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-02T18:27:16.355762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-02T18:27:26.786752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
alcohol consumption per capitatobacco use totalwomen marriage by 18men marriage by 18currently married women ages 15-49birth ratelife expectancydeath rateagriculture occupation ratioindustry occupation ratioservices occupation ratiocontraceptive prevalencemothers mean age at first birthhealth expendituresgdp per capitaliteracy totalphysicians densitypopulation below povertypopulation sizecontinent
alcohol consumption per capita1.0000.207-0.1400.109-0.482-0.6200.4810.559-0.5170.1190.4700.5150.6330.4440.5640.5020.564-0.341-0.1540.349
tobacco use total0.2071.000-0.417-0.0420.073-0.4920.3030.286-0.2710.2350.2390.1700.4810.2540.2800.4970.426-0.377-0.0980.302
women marriage by 18-0.140-0.4171.0000.5680.2110.622-0.5760.0020.549-0.206-0.431-0.322-0.820-0.415-0.579-0.668-0.6670.5340.2520.272
men marriage by 180.109-0.0420.5681.0000.0320.040-0.1160.030-0.044-0.0900.0370.069-0.401-0.084-0.065-0.197-0.2120.0250.1580.329
currently married women ages 15-49-0.4820.0730.2110.0321.0000.278-0.319-0.1400.478-0.126-0.379-0.343-0.351-0.284-0.350-0.215-0.2800.0320.2340.160
birth rate-0.620-0.4920.6220.0400.2781.000-0.813-0.4360.739-0.444-0.598-0.654-0.851-0.445-0.830-0.789-0.8520.6750.1380.460
life expectancy0.4810.303-0.576-0.116-0.319-0.8131.0000.063-0.7900.3640.6220.5960.8590.4590.8530.6730.779-0.642-0.1520.350
death rate0.5590.2860.0020.030-0.140-0.4360.0631.000-0.2340.2110.2700.1460.4230.3500.2310.2390.373-0.1870.0240.300
agriculture occupation ratio-0.517-0.2710.549-0.0440.4780.739-0.790-0.2341.000-0.411-0.849-0.579-0.823-0.534-0.852-0.668-0.7710.5140.2150.358
industry occupation ratio0.1190.235-0.206-0.090-0.126-0.4440.3640.211-0.4111.0000.2230.3440.4500.0950.4360.3110.358-0.3460.0860.183
services occupation ratio0.4700.239-0.4310.037-0.379-0.5980.6220.270-0.8490.2231.0000.4870.7380.5360.6720.5940.707-0.315-0.2730.297
contraceptive prevalence0.5150.170-0.3220.069-0.343-0.6540.5960.146-0.5790.3440.4871.0000.4710.3870.5670.4820.598-0.4890.2520.267
mothers mean age at first birth0.6330.481-0.820-0.401-0.351-0.8510.8590.423-0.8230.4500.7380.4711.0000.5750.8640.7470.813-0.703-0.2350.398
health expenditures0.4440.254-0.415-0.084-0.284-0.4450.4590.350-0.5340.0950.5360.3870.5751.0000.3830.3810.521-0.242-0.1720.266
gdp per capita0.5640.280-0.579-0.065-0.350-0.8300.8530.231-0.8520.4360.6720.5670.8640.3831.0000.7120.829-0.682-0.1540.273
literacy total0.5020.497-0.668-0.197-0.215-0.7890.6730.239-0.6680.3110.5940.4820.7470.3810.7121.0000.841-0.607-0.2910.262
physicians density0.5640.426-0.667-0.212-0.280-0.8520.7790.373-0.7710.3580.7070.5980.8130.5210.8290.8411.000-0.620-0.0950.386
population below poverty-0.341-0.3770.5340.0250.0320.675-0.642-0.1870.514-0.346-0.315-0.489-0.703-0.242-0.682-0.607-0.6201.000-0.0750.297
population size-0.154-0.0980.2520.1580.2340.138-0.1520.0240.2150.086-0.2730.252-0.235-0.172-0.154-0.291-0.095-0.0751.0000.073
continent0.3490.3020.2720.3290.1600.4600.3500.3000.3580.1830.2970.2670.3980.2660.2730.2620.3860.2970.0731.000

Missing values

2023-06-02T18:27:18.972580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-02T18:27:19.339366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-02T18:27:19.662226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

alcohol consumption per capitatobacco use totalwomen marriage by 18men marriage by 18currently married women ages 15-49birth ratelife expectancydeath rateagriculture occupation ratioindustry occupation ratioservices occupation ratiocontraceptive prevalencemothers mean age at first birthhealth expendituresgdp per capitaliteracy totalphysicians densitypopulation below povertypopulation sizecontinent
country
Afghanistan0.0123.328.3NaN70.334.7954.0512.0844.318.137.618.919.915.51500.037.30.2554.539232003.0Asia
Albania4.4022.411.81.267.212.4879.707.3641.418.340.346.026.66.714500.098.41.8814.33101621.0Europe
Algeria0.5921.03.8NaN56.017.8478.274.3310.830.958.453.6NaN6.311000.081.41.725.544758398.0Africa
Andorra10.9931.8NaNNaNNaN6.8783.617.980.54.495.1NaN32.89.149900.0100.03.33NaN85468.0Europe
Angola5.84NaN30.36.055.741.4262.517.8085.015.0NaN13.719.42.95900.071.10.2132.335981281.0Africa
Antigua and Barbuda11.88NaNNaNNaN31.515.0178.045.697.011.082.0NaNNaN5.619100.099.02.76NaN101489.0North America
Argentina7.9524.5NaNNaN48.915.3878.557.285.328.666.170.1NaN10.021500.099.04.0635.546621847.0South America
Armenia3.7725.55.30.464.810.8076.409.5436.317.046.757.125.212.214200.099.84.4026.42989091.0Asia
Australia9.5113.6NaNNaN55.912.2383.286.763.621.175.366.928.710.749800.0NaN4.13NaN26461166.0Oceania
Austria11.9026.4NaNNaN58.79.3982.489.860.725.274.179.029.711.554100.0NaN5.2913.38940860.0Europe
alcohol consumption per capitatobacco use totalwomen marriage by 18men marriage by 18currently married women ages 15-49birth ratelife expectancydeath rateagriculture occupation ratioindustry occupation ratioservices occupation ratiocontraceptive prevalencemothers mean age at first birthhealth expendituresgdp per capitaliteracy totalphysicians densitypopulation below povertypopulation sizecontinent
country
United Kingdom9.8015.40.1NaN50.710.8082.059.121.315.283.576.129.012.045000.0NaN3.0018.668138484.0Europe
United States8.9323.0NaNNaN51.912.2180.758.420.720.337.373.927.018.863700.0NaN2.6115.1339665118.0North America
Uruguay5.4221.5NaNNaN55.412.6578.669.1213.014.073.079.6NaN9.222800.098.84.948.83416264.0South America
Uzbekistan2.4517.6NaNNaN68.615.1875.555.4325.913.260.9NaN23.76.87700.0100.02.3714.131360836.0Asia
Vanuatu1.6017.8NaNNaN69.221.1975.403.9965.05.030.049.0NaN4.02800.089.10.17NaN313046.0Oceania
Venezuela2.51NaNNaNNaN51.516.9974.256.557.321.870.975.0NaN3.87704.097.51.7333.130518260.0South America
Vietnam3.4124.8NaNNaN72.615.2975.795.7740.325.734.072.8NaN4.710600.095.80.836.7104799174.0Asia
Yemen0.0220.3NaNNaN60.424.0567.835.54NaNNaNNaN33.520.84.32500.070.10.5348.631565602.0Asia
Zambia3.8214.429.02.853.334.4866.606.0254.89.935.349.619.25.63200.086.71.1754.420216029.0Africa
Zimbabwe3.1111.733.71.961.632.7763.798.5167.57.325.266.820.33.42100.089.70.2038.315418674.0Africa